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  "Title": "Measuring the Stability of Dimension Reduction and Cluster\nAssignment in scRNA-Seq Experiments",
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  "Authors@R": "person(\"Ben\", \"Abrahams\", , \"ben.abrahams.de@gmail.com\", role = c(\"aut\", \"cre\"))",
  "Description": "Provides functions for evaluating the stability of\nlow-dimensional embeddings and cluster assignments in\nsingle‑cell RNA sequencing (scRNA‑seq) datasets. Starting from\na principal component analysis (PCA) object, users can generate\nmultiple replicates of t‑Distributed Stochastic Neighbor\nEmbedding (t‑SNE) or Uniform Manifold Approximation and\nProjection (UMAP) embeddings. Embedding stability is quantified\nby computing pairwise Kendall’s Tau correlations across\nreplicates and summarizing the distribution of correlation\ncoefficients. In addition to dimensionality reduction,\n'scStability' assesses clustering consistency using either\nLouvain or Leiden algorithms and calculating the Normalized\nMutual Information (NMI) between all pairs of cluster\nassignments. For background on UMAP and t-SNE algorithms, see\nMcInnes et al. (2020, <doi:10.21105/joss.00861>) and van der\nMaaten & Hinton (2008, <https://github.com/lvdmaaten/bhtsne>),\nrespectively.",
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